The use of multiple transmit/receive antennas in wireless networks promises mitigation of interference and high spectral efficiencies through concentrating signals along a designated direction or transmission path. Compared to single-antenna-to-single-antenna transmissions, transmit beamforming may yield increased range (e.g., an N-fold increase for free space propagation), increased rate (e.g., an N2-fold increase in a power-limited regime), increased power efficiency (e.g., an N-fold decrease in the net transmitted power for a fixed received power), and/or may allow splitting high data-rate stream into multiple lower data-rate streams. (Here, N is the number of cooperative nodes or antenna elements at the transmit side.)
Distributed coherent transmit beamforming is a form of cooperative communication in which two or more information sources simultaneously transmit a common message, controlling the phase of their transmissions so that the signals constructively combine at an intended destination. Collective digital beamforming implementation in a decentralized network may require distributed algorithms for coordinating the pre-coding matrices used by each element of the arrays of transmit antennas with low overhead. Such distributed transmit beamforming methods often rely on complex weighting algorithms and explicit feedback of the weights from the receiver to the transmitter based on Line-of-Sight (LoS) combination, to shape the collected radiation beam. The implicit transmit beamforming weights may be based on link metrics such as packet error rate and signal-to-noise ratio (SNR), which are not effective in MP environment. By fixing the phase and power radiated by each of the N transmit antennas, up to N2 fold gain can be reached at the receiver. Perfect channel state information (CSI) at the transmitter may be required by conventional transmit beamforming schemes to generate beamforming coefficients and achieve phase alignment at the receiver. In full-feedback closed-loop synchronization, each user uses a single beam and a linear filter at the receiver, while leveraging perfect channel state information (CSI) at the transmitters and receivers. Alternatively, channel training in the forward direction is sent using the current beam-formers and used to adapt the receive filters. Training in the reverse direction is sent using the current receive filters as beams and used to adapt the transmit beamformers. This approach directly estimates the optimal beamformer and receive filter parameters, as opposed to estimating the CSI needed to compute those coefficients. In this approach, neither the transmitters nor the receiver may have perfect channel state information, but there is a low-rate feedback link from the receiver to the transmitters, to adjust nodes' phases for all radios/sensors simultaneously, in each time slot, to achieve phase alignment.
The resultant beam shape at the receiver may resemble a phased-array radiation pattern, with one main lobe and multiple undesired side lobes that cause interference at other nodes. With these techniques, it may be difficult or impossible to support coherent addition of wave-fronts in MP environments, since most distributed beamforming approaches assume LoS links between transmitters and receiver.
Furthermore, the distributed beamforming algorithms may take hundreds of iteration cycles before converging, adding delay and making real-time network adaptation challenging. Also, the iterative algorithms may fail to converge in dynamic channels and other challenging environments.
Thus, selected current distributed communication and networking approaches may suffer from a number of disadvantages, including these:
(1) difficult operation in multipath (MP) and non-line-of-sight (NLoS) environments;
(2) need to rely on complex weights and pre-coding matrices derived from link metrics;
(3) increased interference because of undesired multiple side lobes resulting from beamforming;
(4) additional delay and potential non-converge in dynamic and challenging environments, due to a large number of iterative steps;
(5) reliance on exact channel state information (CSI) at the transmitters;
(6) need for channel training in the forward and return directions to estimate weights and pre-coding matrices; and
(7) reliance on exact long term and short term synchronization of carrier, data, and time.
Needs exist for improved communication techniques for distributed coherent communications, and for apparatus and articles of manufacture for such improved communications. Needs also exist for methods, apparatus, and articles of manufacture for hiding transmitter locations in multipath environments in real-time, to prevent hostile receivers from locating signal transmitters, without unduly disrupting communications between the transmitters and their intended receivers. Additional needs exist for improved methods, apparatus, and articles of manufacture that facilitate non-invasive imaging, such as ultrasound imaging.